Papers
1
Total Citations
3
H-Index
1
About
Yue Hua is a robotics researcher whose work centers on multi-sensor fusion for simultaneous localization and mapping (SLAM), with a particular emphasis on enhancing autonomy in complex environments. His major contribution is the development of a tightly coupled LIDAR-inertial-visual SLAM framework that integrates an error state iterative Kalman filter with coarse-to-fine loop closure detection. This approach significantly improves the precision and robustness of transformation estimation, addressing critical challenges in real-time robotic navigation. While his most-cited paper has garnered 3 citations since 2023, the work represents a foundational step toward more reliable multi-sensor systems. Hua’s research is notable for its practical focus on tightly coupled sensor integration, which reduces drift and enhances localization accuracy in dynamic or feature-sparse settings. His contributions are particularly relevant for autonomous vehicles, drones, and mobile robots operating in GPS-denied environments. By advancing the state of the art in sensor fusion, Yue Hua is helping to pave the way for more resilient and perceptive robotic systems.
Research Focus
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Top Papers
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